Latest AI and machine learning research in genetics for healthcare professionals.
Motivation: Modeling inter-omics interactions across multiple molecular levels is critical for deciphering the mechanisms underlying complex diseases. Epigenomic and structural alterations, such as DNA methylation and copy number alterations, modulate gene expression and collectively influence disease progression and patient survival outcomes. Despite advancements in deep learning-based multi-omic...
Mammalian cell lines are the preferred hosts for producing commercially relevant therapeutic proteins such as antibodies, multispecifics, and cytokine fusion proteins. Even though significant investment is made to optimize upstream and downstream processes, the optimal gene design parameters for heterologous recombinant protein expression remain poorly understood. We describe here a generic approa...
Vision-Language Pre-Trained models, notably CLIP, that utilize contrastive learning have proven highly adept at extracting generalizable visual featur...
Multi-modal image fusion aims to consolidate complementary information from diverse source images into a unified representation. The fused image is ex...
Reconstructing dynamical evolution from limited observations is a fundamental challenge in single-cell biology, where dynamic unbalanced optimal trans...
The Cox Proportional Hazards (PH) model is widely used in survival analysis. Recently, artificial neural network (ANN)-based Cox-PH models have been d...
Bulk OMICs data, such as RNA-seq and proteomics, remain foundational in biomedical and cancer research. While single-cell transcriptomics has revoluti...
DNA methylation is a significant epigenetic modification involving the addition of a methyl group to the position 5' of the cytosine residues. The mod...
Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research by enabling transcriptomic analysis at single-cell resolution. Yet, existin...
Adapting Large Language Models (LLMs) to specialized domains without human-annotated data is a crucial yet formidable challenge. Widely adopted knowle...
Autism Spectrum Disorder standardized behavioral assessments provide quantitative measures of symptoms, yet their reliability and consistency have not...
Accurate prediction of gene fusion pathogenicity is critical for understanding oncogenic mechanisms and advancing precision oncology. While existing c...
Neurophysiologists have discovered many mechanisms underlying the production of animal behaviors in specific species; these involve a collection of ne...
Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to impaired insulin production or function. Two main forms...
Background: Accurate determination of genomic biomarkers from tumor sequencing is fundamental to precision oncology, informing disease classification ...
High-dimensional data often exhibit variation that can be captured by lower dimensional factors. For high-dimensional data from multiple studies or en...
Major Depressive Disorder (MDD) is a clinically heterogeneous syndrome with diverse etiological pathways. Traditional Epigenome-Wide Association Studi...
Nanopore sequencing has achieved a new standard of accuracy with the advent of R10.4.1 flow cell and high-performance Transformer-based basecalling mo...
The microbiome responds to physicochemical changes in the environment, making it a sensitive indicator of ecosystem status. Monitoring microbial commu...
Protein expression within oncogenic or suppressive pathways is a hallmark indicator of oncogenesis. While traditional AI models in digital pathology a...